Сборник работ зарубежных специалистов, отражающий современное состояние в новом направлении программирования, тесно связанном с математической логикой. Это направление активно развивается как в теоретическом, так и в практическом плане, включая в себя новые языки (Пролог, Логлисп н др.), методы реализации и проекты машинных архитектур. В сборник включен специально написанный обзор литературы по логическому программированию. Среди авторов известные зарубежные специалисты: Б. Домелки, П. Середи(ВНР), А. Колмероэ (Франция), Дж. Робиисои (США), Р.Ковальский (Великобритания). Для математнков-прикладииков, программистов, аспирантов и студентов университетов.

Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for data mining. This book first sets the necessary backgrounds for the reader, including an overview of data mining, evolutionary algorithms and inductive logic programming. It then describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammars. The formalism is powerful enough to represent context- sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the knowledge induced. A grammar-based genetic programming system called LOGENPRO (The LOGic grammar based GENetic PROgramming system) is detailed and tested on many problems in data mining. It is found that LOGENPRO outperforms some ILP systems. We have also illustrated how to apply LOGENPRO to emulate Automatically Defined Functions (ADFs) to discover problem representation primitives automatically. By employing various knowledge about the problem being solved, LOGENPRO can find a solution much faster than ADFs and the computation required by LOGENPRO is much smaller than that of ADFs. Moreover, LOGENPRO can emulate the effects of Strongly Type Genetic Programming and ADFs simultaneously and effortlessly. Data Mining Using Grammar Based Genetic Programming and Applications is appropriate for researchers, practitioners and clinicians interested in genetic programming, data mining, and the extraction of data from databases.

The purpose of this book is to demystify the techniques associated with the field of artificial intelligence. It covers both the theory and the practical applications to teach developers how to apply AI techniques in their own designs.

This text offers guidance on how and when to use a particular software tool (with their companion data sets) from among the hundreds offered when faced with a data set to mine.

Building Intelligent .NET Applications is an introduction to the world of Artificial Intelligence (AI) for .NET programmers. It is the first book to show professional .NET developers how to incorporate AI into their daily programming. In this accessible guide, developers will learn how to enhance both new and existing .NET applications with intelligent agents, data mining, rule-based systems, and speech processing.

Armed with the knowledge contained in this book, businesses can collect and analyze Web-based data to help develop customer relationships, increase sales, and identify existing and potential threats.

Natural language parsing was one of the main motivations from which Prolog was developed Metamorphosis grammars, designed by Alain Colmerauer at GIA Marseille, was the first logic-based formalism for natural language parsing These grammars have inspired a number of other logic-based grammar formalisms and implementations which are now commonly used and some of which are still under intense research. These formalisms are very close per se to Metamorphosis grammar, some of them were motivated by the need to enhance the ease of writing grammar rules, others support deeper linguistic motivations and concepts. Natural language semantics and pragmatics are now two major fields in linguistics, philosophy as well as artificial intelligence and, obviously, computational linguistics. Surprisingly enough, up to now, this area was somewhat neglected, except by a small number of researchers among us. With the development of large and efficient Prolog interpreters and compilers and with the expansion of the theoretical aspects of Logic Programming, the study of natural language semantics and related pragmatic aspects is now becoming a very attractive topic.

Prolog is a logical and a declarative programming language. The name itself, Prolog, is short for PROgramming in LOGic. Prolog's heritage includes the research on theorem provers and other automated deduction systems developed in the 1960s and 1970s. The inference mechanism of Prolog is based upon Robinson's resolution principle (1965) together with mechanisms for extracting answers proposed by Green (1968). These ideas came together forcefully with the advent of linear resolution procedures. Explicit goal-directed linear resolution procedures, such as those of Kowalski and Kuehner (1971) and Kowalski (1974), gave impetus to the development of a general purpose logic programming system. The "first" Prolog was "Marseille Prolog" based on work by Colmerauer (1970). The first detailed description of the Prolog language was the manual for the Marseille Prolog interpreter (Roussel, 1975). The other major influence on the nature of this first Prolog was that it was designed to facilitate natural language processing.

Prolog was invented in the early seventies by Alain Colmerauer and is colleagues at the University of Marseille. Prolog stands for Programmation en Logique (Programming in Logic). Prolog differs from the most common programming languages because it is a declarative language. This means that the programer specifies a goal which is to be achieved and the Prolog interpreter/compiler works out how to achieve it. Traditional programming languages are said to be procedural. This means that the programmer must specify in detail how to solve a problem. In purely declarative languages, the programmer only states what the problem is and leaves the rest to the language itself. Some applications of Prolog are: intelligent data base retrieval, natural language understanding, expert systems, specification language, machine learning, robot planning, graphics and problem solving . The iProlog version of Prolog includes a number of extensions intended to make it a powerful environment for data mining as well as logic programming. These extensions include a library of machine learning tools, ripple down rules, a frame language and functional notation.

Prolog is a simple but powerful programming language developed at the University of Marseilles [Roussel 75], as a practical tool for programming in logic [Kowalski 74]. From a user's point of view the major attraction of the language is ease of programming. Clear, readable, concise programs can be written quickly with few errors. For an introduction to programming in Prolog, readers are recommended to consult [Sterling & Shapiro 86]. However, for the benefit of those who do not have access to a copy of this book, and for those who have some prior knowledge of logic programming, a summary of the language is included. For a more general introduction to the field of Logic Programming see [Kowalski 79]. See section The Prolog Language. This manual describes a Prolog system developed at the Swedish Institute of Computer Science. Parts of the system were developed by the project "Industrialization of SICStus Prolog" in collaboration with Ericsson Telecom AB, NobelTech Systems AB, Infologics AB and Televerket. The system consists of a WAM emulator written in C, a library and runtime system written in C and Prolog and an interpreter and a compiler written in Prolog. The Prolog engine is a Warren Abstract Machine (WAM) emulator [Warren 83]. Two modes of compilation are available: in-core i.e. incremental, and file-to-file. When compiled, a predicate will run about 8 times faster and use memory more economically. Implementation details can be found in [Carlsson 90] and in several technical reports available from SICS. SICStus Prolog follows the mainstream Prolog tradition in terms of syntax and built-in predicates, and is largely compatible with DECsystem-10 Prolog and Quintus Prolog.

У виданні розглянуто питання створення і застосування систем інтелектуального аналізу даних — дейтамайнінгу. Стисло проаналізовано концепцію дейтамайнінгу, наведено приклади його застосування в економіці й бізнесі, описано найсучасніші пакети програмного забезпечення дейтамайнінгу, викладено питання архітектури, класифікації та стандартів програмних систем дейтамайнінгу, подано розгорнутий аналіз процесів, активностей і методів дейтамайнінгу, зокрема наводиться дерево методів дейтамайнігу та розглядаються методи, засновані на збережених даних і на дистиляції даних. У посібнику розглядаються найвідоміші методи дейтамайнінгу (класгеризація, метод «найближчого сусіда», міркування за прецедентами, візуалізація даних, крос- табуляція, довірчі мережі, нейронні мережі, генетичні алгоритми). Подаються особливості використання правил і машинних висновків для інтелектуального аналізу даних, докладно описуються різновиди дерев рішень у дейтамайнінгу, розглядаються питання застосування в дейтамайнінгу нечітких запитів (аналізів) та інтелектуальних програмних агентів. Для студентів вищих навчальних закладів усіх спеціальностей бакалаврського та магістерського рівнів, які вивчають дисципліну «Інтелектуальний аналіз даних» або «Дейтамайнінг (Data Mining)», споріднені дисципліни, в яких розглядаються окремі теми з цих проблем. Видання може бути корисним аспірантам та спеціалістам відповідного профілю.